This description relates to assessing features for classification.
Classifications may be useful, for example, in the medical field, to classify an image, a test result, a genetic sequence, or a radiograph as being indicative of either a disease or no disease. In the security field, it may be useful to classify an x-ray as one that indicates either that a threatening object was present in the space imaged or that no threatening object was present. In the communications field, it may be useful to classify a sound as being a human voice or as being other noise.
Frequently, classification uses values of features, each feature relating to a characteristic of data that represents a case being examined (e.g., a lesion, a potential bomb, or a sound signal). For example, features of a multispectral image (e.g., of a skin lesion or of suitcase contents) may include an area, a contrast, a perimeter length, a color variability, a color intensity, a perimeter-to-area ratio, an asymmetry, a border irregularity, and a diameter of the lesion. Features of a sound file may include an amplitude at a frequency or a variation in volume or frequency amplitude across time. A value for each of the features may be determined for each case (e.g., for each lesion based on an image of the lesion) that belongs to a set of cases. For example, if there are ten images (cases) of skin lesions in a data set, a value for the area may be determined for the lesion of each image.
Different features may be more or less useful in effectively classifying cases of a data set (e.g., whether a lesion is melanoma, whether a suitcase-content item is a bomb, or whether a sound signal is a human voice).